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Contents lists available at ScienceDirect
International Journal of Medical Informatics
journal homepage: www.ijmijournal.com
Review article
Promising approaches of computer-supported dietary assessment and
management—Current research status and available applications
Andreas G. Arens-Volland a,∗ , Lübomira Spassova a , Torsten Bohn b
a
Luxembourg Institute of Science and Technology, IT for Innovative Services (ITIS) Department, 5, avenue des Hauts-Fourneaux, L-4362 Esch/Alzette,
Luxembourg
b
Luxembourg Institute of Science and Technology, Environmental Research and Innovation (ERIN) Department, 41, rue du Brill, L-4422 Belvaux,
Luxembourg
a r t i c l e
i n f o
Article history:
Received 17 November 2014
Received in revised form 11 August 2015
Accepted 14 August 2015
Available online xxx
Keywords:
Dietary records
Food diaries
Self-management
Food intake
Personal health records
Ubiquitous and mobile devices
a b s t r a c t
Purpose: The aim of this review was to analyze computer-based tools for dietary management (including
web-based and mobile devices) from both scientific and applied perspectives, presenting advantages and
disadvantages as well as the state of validation.
Methods: For this cross-sectional analysis, scientific results from 41 articles retrieved via a medline search
as well as 29 applications from online markets were identified and analyzed.
Results: Results show that many approaches computerize well-established existing nutritional concepts
for dietary assessment, e.g., food frequency questionnaires (FFQ) or dietary recalls (DR). Both food records
and barcode scanning are less prominent in research but are frequently offered by commercial applications. Integration with a personal health record (PHR) or a health care workflow is suggested in the
literature but is rarely found in mobile applications.
Conclusions: It is expected that employing food records for dietary assessment in research settings will be
increasingly used when simpler interfaces, e.g., barcode scanning techniques, and comprehensive food
databases are applied, which can also support user adherence to dietary interventions and follow-up
phases of nutritional studies.
© 2015 Elsevier Ireland Ltd. All rights reserved.
Contents
1.
2.
3.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
2.1.
Data sources and search terms employed for article and app search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
2.2.
Selection and exclusion criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
2.3.
Data extraction and analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
3.1.
General aspects of computer-supported dietary management and state of validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
3.2.
Computer-supported dietary management for overweight, obesity, and weight-loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
3.2.1.
Scientific approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
3.2.2.
Computer programs and mobile apps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
3.3.
Computer-supported dietary management for diabetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
3.3.1.
Scientific approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
3.3.2.
Mobile apps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
Abbreviations: 24-HDR, 24-hour dietary recall; DHQ, dietary history questionnaire; DR, dietary recall; FDA, food and drug administration; FFQ, food frequency questionnaire; FNDDS, food and nutrient database for dietary studies; FR, food records; ICT, information and communication technology; mHealth, mobile health; NCI, National
Cancer Institute; NICE, National Institute for Health and Clinical Excellence; PDA, personal digital assistant; PHR, personal health record; RCT, randomized controlled trial;
T1DM, type 1 diabetes mellitus; T2DM, type 2 diabetes mellitus; TADA, technology assisted diet assessment; USDA, United States Department of Agriculture.
∗ Corresponding author. Fax: +352 42 59 91 333.
E-mail address: [email protected] (A.G. Arens-Volland).
http://dx.doi.org/10.1016/j.ijmedinf.2015.08.006
1386-5056/© 2015 Elsevier Ireland Ltd. All rights reserved.
Please cite this article in press as: A.G. Arens-Volland, et al., Promising approaches of computer-supported dietary
assessment and management—Current research status and available applications, Int. J. Med. Inform. (2015),
http://dx.doi.org/10.1016/j.ijmedinf.2015.08.006
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2
4.
5.
Integrative summary and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
4.1.
Principal results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
4.2.
Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
Conclusions and perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
Conflicts of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
Funding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
Authors’ contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 00
1. Introduction
Diet-related chronic health complications, such as obesity, diabetes, or food hypersensitivities are major public health burdens.
According to a WHO report from 2004, diseases with major nutritional determinants make up 41% of disability-adjusted life years
among all diagnosed diseases in Europe [1]. A healthy diet is a
key component of a healthy lifestyle that can prevent the onset
of chronic diseases or mitigate their severity. However, despite
many efforts by national and international nutrition organizations
to promote healthy eating behavior, the prevalence of e.g. obesity, cardiovascular diseases and diabetes is still increasing in most
westernized but also in developing countries, an observation that
has been related to a too high consumption of total calories [2], too
many sugars [3], high sodium intake [4], and an insufficient intake
of dietary fiber [2,5], among others.
In general, tackling the problem of being overweight and
obese is perceived as a difficult target, typically requiring complex
lifestyle changes with multi-dimensional support with respect to
psychological, social, and clinical aspects, including dietary support
[6]. A lot of research has thus been carried out on means promoting
behavioral changes, including personalized strategies such as goal
setting and self-monitoring [7]. In addition to recording physical
activity, self-monitoring involves the capturing of dietary intake to
help individuals to become aware of their current behavior. Early
computer-tailored dietary behavior interventions were introduced
in the 1990s [8] and have become increasingly popular during the
last decade [9]. The advent of portable technologies such as personal digital assistants and smartphones has particularly propelled
research activities applying mobile health (mHealth) approaches
in the field of diet management.
Although evidence for the efficacy of mHealth is generally sparse
[10], research has indicated that the use of hand-held devices
can improve the dietary intake of healthy food groups such as
whole grains and vegetables [11]. The use of mHealth technology
also has the potential to reduce health care costs and to improve
well-being in numerous ways, for example through continuous
health monitoring, encouraging healthy patterns, and supporting
self-management [10,12,13]. In their systematic review, Kroeze
et al. [14] concluded that there is strong evidence in favor of
computer-tailored interventions for improving dietary behavior.
These findings have also been supported by Long et al. in their 2010
review on technology employed for dietary assessment [15].
In 2009, Ngo et al. systematically reviewed the literature for
studies applying information and communication technology to
dietary assessment [16]. The authors found that most often food
frequency questionnaires (FFQ), 24-hour diet recalls (24-HDR) and
diet histories have been applied in ICT. To a lesser extent, food
records (FR) or taking photos of foods were used. Rusin et al. looked
at logging techniques for measuring food intake, such as typing
in or selecting a food type from a database [17]. They concluded
that very few barcode-based solutions are available and that most
systems share information via e-mail, which cannot be seen as an
integrated solution. Their review, however, neglected input types
other than textual, such as photo documentation, which has also
been used in dietary assessment [18,19] or self-monitoring tools
[20]. The majority of scientific reviews have focused on specific
diseases, such as obesity [21–25] or diabetes [26–33], or on particular application areas, such as nutritional epidemiology [9,34–37].
No cross-sectional analysis of computer-based tools and applied
functions for dietary management exists in the literature.
In this article, we review the different fields in which computeraided dietary assessment has been employed, aiming to give
an overview of the state-of-the-art possibilities of computersupported dietary management techniques from both scientific and
applied perspectives. The specific questions that are addressed in
this review are: (1) What current scientific evidence exists for the
efficacy of computer-supported diet management approaches? (2)
Which functionalities are offered by diet-related mobile apps? (3)
Which similarities and differences between scientific approaches
and available apps are there in terms of requirements concerning specific diseases? and (4) Which gaps exist between scientific
research and commercially available applications in the respective
areas?
It needs to be stressed that an analysis of any psychological
aspects, such as social interactions or stress, which undoubtedly
play an important role in computer-supported diet management,
is beyond the scope of this article.
2. Methods
2.1. Data sources and search terms employed for article and app
search
PubMed was searched to retrieve articles written in English and
related to computer-supported dietary management approaches
among adults and children (Fig. 1). There were no boundaries set
for the time interval, as diet-related research involving computerbased technologies was expected to be rather novel. The search
was performed between September 2013 and April 2014, and
titles and abstracts of articles were evaluated. Different search
terms were selected to represent information and communication technology (ICT): “mobile Health”, “PDA”, “mobile computer”,
“smartphone”, “handheld”, “cell phone”, “Internet”, “computer”,
“web-based”, “website”, target domains of nutrition and health:
“diet”, “healthy eating”, “eating”, “nutrition”, “food”, and nutritionrelated diseases or conditions: “obesity”, “overweight”, “weight
loss”, and “diabetes”. Using the PubMed advanced search interface, terms describing ICT were OR-combined and joined with
OR-combined terms describing the target domains. The retrieved
articles were then evaluated for additional referenced sources. In
addition to PubMed, online markets for iOS and Android applications were searched, using similar terms as mentioned above for
the diet-related conditions and nutrition domains.
2.2. Selection and exclusion criteria
Only articles published in scientific peer reviewed journals and
full papers from conference proceedings were included. Thematically, any publication related to some form of dietary management
Please cite this article in press as: A.G. Arens-Volland, et al., Promising approaches of computer-supported dietary
assessment and management—Current research status and available applications, Int. J. Med. Inform. (2015),
http://dx.doi.org/10.1016/j.ijmedinf.2015.08.006
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Research studies:
-
3
Applications (commercially
available):
Pubmed
All years until present
Scientific (peer
reviewed) journals +
full paper proceedings
-
Apple iPhone apps
-
Android apps
“Communication technology”: mobile Health, PDA,
mobile computer, smartphone, handheld, cell
phone, Internet, computer, web-based, web-site
“Target domains”: diet, healthy eating, eating,
nutrition, food
“Diet-related diseases”: obesity, overweight,
weight-loss, diabetes
2602 articles
- with abstracts
Filter on title: only individuals. No
institutions, not merely educational
370 articles remaining
Filter: further abstract and manuscript
screening; exploration of references
41 articles remaining
-
10 reviews
-
31 original research
29 mobile applications
Fig. 1. Selection process of studies and applications utilizing computer-aided dietary management.
using ICT offered to individual end users was included. Thus, all
publications irrespective of their types of study design, participant selection, and outcome measures have been considered in this
review.
Approaches targeted to worksite or school settings were
excluded. Publications claiming to use Internet resources were also
included, as recent mHealth approaches are often built upon webbased approaches. Any applications including diet management
functionalities, such as diet assessment, dietary advice, diet and
menu planning, social interaction and integration into health care
workflow, i.e. communication with a counselor or synchronization
with personal health records (PHR), were considered, while merely
educational applications were left out.
2.3. Data extraction and analysis
The PubMed search retrieved 2602 articles with available
abstracts. After a first screening of the titles, we rejected papers that
were clearly not related to diet management as described above, so
that 370 articles remained for further review. Based on an evaluation of the corresponding abstracts, 36 articles finally remained for
in-depth evaluation, for which full text documents were retrieved
and analyzed. Through exploring their reference lists, 5 additional
articles were identified, resulting in a total number of 41 articles, of
which 10 were reviews and the remaining 31 were original research
articles. A summary of these articles’ characteristics can be seen in
Table 1.
Please cite this article in press as: A.G. Arens-Volland, et al., Promising approaches of computer-supported dietary
assessment and management—Current research status and available applications, Int. J. Med. Inform. (2015),
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4
Table 1
Overview of studies employing ICT for dietary management.
Area
No. of research
articles reviewed
Type and no. of applied diet
management approach
Type and no. of applied
technology
References
General dietary
assessment
10
Dietary recall: 4
Food record: 6
[11,18,19,24,38–43]
Obesity, overweight,
weight-loss
12
Diabetes (type 1 and 2)
3
Dietary recall: 2
Food record: 5
Self-management: 5
Food record: 2
Menu planning:1
Validation/Epidemiology
5
PDA: 2
Mobile phone: 5
Web-based: 3
PDA: 4
Mobile phone: 4
Web-based: 4
PDA: 1
Mobile phone: 1
Web-based: 1
Web-based: 4
Mobile phone: 1
Dietary recall: 3
Food record: 2
[21–23,37,44–51]
[19,26,28]
[35–37,52,53]
Abbreviations: PDA: personal digital assistant.
Table 2
Characteristics of reviewed commercially available mobile applications in the area of dietary management.
Area
No.
Input techniques
Diet recommendation/menu
planning
Integration with social
network, HCP, or data export
Major missing aspects/shortcomings
Obesity,
20
Barcode scan: 6
Meal planning: 4
Social network: 11
No comprehensive underlying
food databases
Recipes: 1
HCP: 5
9
Picture taking: 6
Typing in/selection form a list: 14
Speech input: 2
Barcode scan: 2
Recipes: 2
Social network: 4
overweight,
weight-loss
Diabetes
Picture taking: 2
Typing in/selection form a list: 9
HCP/data export: 8
Most apps not approved as
medical applications;
Diet recommendations and
menu planning functionalities
are missing;
Integration of PHR is missing
Abbreviations: PHR: personal health record; HCP: health care professional.
In addition, we reviewed a total of 29 mobile applications (apps)
related to diet management in the relevant fields as described
above, which were available in application stores. Table 2 describes
the characteristics of the evaluated apps.
3. Results
3.1. General aspects of computer-supported dietary management
and state of validation
In a structured review on dietary assessment technologies in
nutritional epidemiology by Illner et al. [9] published in 2012, the
authors identified the real-time food recording capability as the
main advantage of smartphones in the context of eating events,
i.e., during meals. However, the validity of dietary intake assessed
with this technology remains uncertain. Predominant advantages
include the cost- and time-effectiveness as well as a decreased
effort in terms of data collection and a high user acceptance. According to the authors, many epidemiological studies have favored
self-administered FFQs, which are poorly validated and include a
high number of systematic and random measurement errors, such
as no quantification or an imprecise estimation of portion sizes.
However, self-administered FFQs have the advantage of being less
time consuming, and they are easier to integrate into the individual
lifestyle without perturbing the personal eating patterns. On the
other hand, 24-HDRs are known for their high validity and good
measurement properties, but they are quite expensive when used
as a main instrument, and due to their short time period covered
[9] they need some repetitions or a large number of participants
in order to balance out possible fluctuations. As a consequence,
computer- and web-based technologies have emerged to facilitate
the application of 24-HDRs to large populations in a cost-saving
manner.
In a 2007 systematic review by Norman and Zabinski [54], in
which eHealth interventions aiming at improving physical activity
and/or healthy eating were reviewed, the most prominent solution
to assess dietary behavior was the use of self-report FFQ or dietary
recalls. This finding is supported by the 2006 work of Kroeze et al.
[14], which further emphasized the fact that FR were not widely
used in the mid of the last decade as compared to FFQ. Finally,
Norman et al. identified an improved dietary behavior resulting
in significant weight loss of subjects that were allowed to share
their collected data with health professionals and were able to
receive timely and personalized feedback. Unfortunately, concrete
numbers were not reported.
Leatherdale and Laxer performed a validation study [53] to test
for the reliability and validity of the web-based FFQ eaTracker,
developed by the Canadian national professional association for
dietitians. For this purpose, 178 students in Ontario (Canada) used
the eaTracker consumption diary [55] on a daily basis for a period
of one week. The authors found that the dietary intake measures
were accurate, thus supporting its potential use in research studies
where other objective measures are not possible due to large-scale
cohorts.
In a 2009 validation study of a web-based, pictorial version of
the US National Cancer Institute (NCI) paper-based diet history
questionnaire (DHQ) developed by Beasley et al. [36], the authors
found that the web-based version yielded similar repeatability and
validity compared to the paper-based version, when used by 218
participants in randomized order. The study revealed a stable relationship between DHQ and other food intake measurement tools,
such as FR or 24-HDR. As a consequence, the practical advantages of
a web-based DHQ, such as remote administration, immediate nutrient analysis or a potential reduction of missing responses, may lead
to its further use in research.
Subar and colleagues [39,40] developed the web-based Automated Self Administered 24-HDR (ASA24) for adults. The
Please cite this article in press as: A.G. Arens-Volland, et al., Promising approaches of computer-supported dietary
assessment and management—Current research status and available applications, Int. J. Med. Inform. (2015),
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respondents reported their meals through searching or browsing
for foods in a hierarchical list, and afterwards, the portion sizes
were estimated using digital food images. This approach has been
continued in a version targeted at children [56], who are an important target group as eating behavior may be best influenced at early
age, and children have difficulties articulating their eating patterns
by means usually applied to adults, such as dietary records.
In 2011, Arab et al. validated the web-based DietDay 24-hour
recall against the established NCI DHQ, using the doubly labeled
water method with 233 healthy adults aged 21–69 years and
found that the web-based 24-HDR could provide cost-effective
valid dietary intake reports [35]. The authors found that the validity
of web-administered recalls was superior to paper-based FFQ with
respect to delivering reproducible results across different ethnic
groups.
In the framework of the technology assisted dietary assessment (TADA) project [38] of the Purdue University, the research
group around Carol Boushey developed methods for food identification and portion estimation [41,42] using pictures taken on
mobile phones. The image analysis consisted of segmentation, feature extraction, classification, volume estimation of portion size,
and finally, calorie and nutrient estimation using the food and nutrient database for dietary studies (FNDDS) curated by the United
States Department of Agriculture. Early pilot trials [18,24] suggested good usability of this mobile phone food record, although
the authors admitted that further research is needed in order to
increase the accuracy of volume estimation of the approach. Unsurprisingly, it was found that mobile phone FR may be most likely
adopted by adolescents, as these are the most enthusiastic users
and require the least training to provide accurate diet assessment
as compared to adults, who are less efficient, i.e. taking more time
until reaching the same skill level.
In summary, many diverse approaches for computerized diet
management are being pursued: first, well established and originally paper-based research tools such as FFQ, DHQ, 24-HDR are
translated into their respective electronic counterparts, whereas
the application of electronic FR is still on a quite low level. Recalls
and FFQs are useful in population-based studies, but in clinical studies, the preferred dietary assessment method is FR [38]. Through
phone- and picture-based approaches, such as those developed in
the TADA project, electronic FR might replace the currently used
traditional FR methods.
3.2. Computer-supported dietary management for overweight,
obesity, and weight-loss
3.2.1. Scientific approaches
Applications targeting weight monitoring and a balanced diet
constitute the predominant part of computer-aided diet management. Bacigalupo et al. [57] systematically reviewed randomized
controlled trials (RCT) applying mobile technologies for selfmonitoring activities in overweight and obese subjects. The
reviewed seven trials showed consistent evidence for short and
medium-term weight-loss through the use of mobile technology
as part of the intervention delivery.
Hutchessen et al. observed in a 2013 small-scale pilot study [58]
with nine participants that the estimated energy intake obtained
by web-based FR is consistent with other published dietary intake
methods, such as total energy expenditure measured by the doubly
labeled water method, reaching an accuracy of 79.6% (SD = 14.1%).
In a retrospective analysis [48] with 2979 women and 642 men,
Johnson et al. were already able to show in 2011 that participants
in the top third of engagement with electronic food diaries were
more likely to achieve clinically significant weight losses, i.e., over
5% of initial body weight.
5
Self-monitoring is a crucial and recognized factor for obesity
treatment. Regular interaction with a counselor (human or automatic) has shown to improve the results of weight loss programs
[59]. Research has demonstrated that electronic self-monitoring,
i.e., recording food intake and physical activity, is more effective
in terms of weight-loss than the more cumbersome paper diaries
[45,51], as it is easier to use and less time consuming. Burke et al.
were able to show in a 24-month RCT with 210 subjects aged
18–59 years that a combination of electronic self-monitoring and
daily feedback tailored to the captured data and providing positive
stimulations in form of motivational messages, resulted in the highest user adherence levels (90%) and achieved weight-loss (63%),
compared to groups with no intervention (46% and 55%) or only
electronic self-monitoring (80% and 49%). In another 2011 review
by Burke et al. on self-monitoring activities for weight-loss, analyzing US-based studies [7], the authors found that through date
and time stamping, an objective validation of the self-monitoring
behavior could be achieved. Extensive databases compiling information about foods and restaurant dishes eliminated the necessity
to look up and calculate the sum of nutrients and calories. In
addition, the possibility to store frequently consumed food items
eliminated the need for repeatedly searching identical entries.
Already in 2007, Yon et al. [50] tried to confirm the advantage of personal digital assistants (PDA) in a 24-week behavioral
weight-loss study with 61 obese and overweight subjects using
Calorie King’s Diet Diary software on a PDA, compared to 115 similar subjects equipped with paper-based food diaries. Almost half
of the participants (44%) complained about the PDA and the provided software due to shortcomings when trying to find commonly
consumed foods. However, as no significant differences in weightloss or diet self-monitoring (measured in% of weekly FR submitted)
between the two groups were found, the authors concluded that
PDAs were at least comparable to traditional diaries.
In a 2010 review of efficient technology-based weight-loss
interventions [60], Khaylis et al. identified five key components that
effectively drive technology-supported weight loss and determined
its successful use: (1) self-monitoring, (2) frequent counselor feedback and communication, (3) social support, (4) use of a structured
program, and (5) use of an individually tailored program allowing
to adjust to the personal lifestyle.
In this respect, Krukowski et al. [61] recognized the “feedback”
factor (progress charts, physiological calculators, and past journals)
as the best predictor for efficient weight loss during the intervention time, here of up to 12 months. The “social support” factor on
the other hand was the best predictor for maintaining weight-loss
after the intervention, which may explain the somewhat mixed
results for long-term studies, in case social support may not be
present. Table 3 summarizes the research body on successful use
of computer-supported diet management.
3.2.2. Computer programs and mobile apps
A large number of the food-related health and fitness apps that
are commercially available in different app stores, such as iTunes or
Google Play, are related to calorie counting and weight-loss. Food
and calorie trackers, such as “MyFitnessPal”, “Lose It!”, or “Calorie
Count” etc., allow users to log their daily food intake, define personal weight loss goals and review and analyze the gathered data.
One of the critical issues in this context is the entry of new items
into the food diary. Given the huge amount of possible food items, it
is a particular challenge to implement an easy-to-use interface for
food logging. For this purpose, many apps, such as “Food Scanner”,
“FitDay” or “Foodzy”, have incorporated custom food databases
containing nutritional information about a number of food products and offering different options to access this data, such as via
manual search by typing in product names or hierarchical search
through food categories. Some apps, such as “Calorie Count” or
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assessment and management—Current research status and available applications, Int. J. Med. Inform. (2015),
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Subject characteristics
Results
Acharya [21], 2011, USA
PDA-based dietary record
6-month RCT; comparison between PDA- and
paper-based approaches; measures: dietary intake
through 24-HDR, calculated calorie intake, body
weight
192 white obese subjects aged
18–59
Anton [22], 2012, USA
Web-based computerized
tracking system for FR,
feedback and messaging
811 healthy, obese/overweight
men and women aged 30–70
Arab [35], 2011, USA
Web-based 24-HDR
Within a 2 year RCT testing the efficacy of four
macronutrient diets, an evaluation of the usage
and effects of the web-based system has been
performed.
Validation study using the doubly labeled water
method; comparison to FFQ results. Measures:
body weight, dietary intake and total energy
expenditure.
Atienza [11], 2008, USA
PDA-based dietary recall and
education
PDA group significantly increased fruit (P = 0.02)
and vegetable (P = 0.04) consumption compared to
paper-based group;
PDA group significantly decreased consumption of
refined grains (P = 0.02) compared to paper-based
group;
Both groups had significant reductions in weight,
energy intake and calories (P < 0.001)
Participants with higher usage of the system
showed higher weight loss (−8.7% of initial body
weight) as compared to those with lower usage
(−5.5%) (P < 0.001)
Web-based dietary recalls offer an inexpensive and
accessible solution for dietary assessment; validity
of web-administered recall was superior to
paper-based FFQ with respect to delivering stable
results across different ethnic groups
Intervention participants reported significantly
higher increased vegetable intake (1-5-2.5
servings/day; P = 0.02) and greater intake of dietary
fiber from grains (3.7–4.5 servings/day; P = 0.10) as
compared to control.
Burke [7,45], 2011, USA
PDA-based self-monitoring
applying FR and feedback
Cadmus-Bertram [62], 2013, USA
Web-based self-monitoring for
overweight/obese women at
increased breast cancer risk
Carter [37], 2012, UK
8 week pilot RCT; intervention group monitored
their vegetable and whole-grain intake using a
PDA; control group received written educational
material related to nutrition in middle-aged and
older adults; measures: dietary intake assessed via
Block FFQ.
6-month RCT; three groups: (a) PDA
self-monitoring, (b) Paper diary/record; (c) PDA
self-monitoring plus feedback; measures:
weight-change after 6 months and adherence over
time
115 black and 118 white
healthy adults aged 21–69
27 subjects aged 50 or older
210 healthy adults aged 18-59
with mean BMI of 34.0 kg/m2
50 overweight/obese women
at increased breast cancer risk
Smartphone-based dietary
assessment applying FR
12-week RCT; intervention group (n = 33) used
SparkPeople website for self-monitoring (goal
setting, tracking diet and exercise); control group
(n = 17) received dietary information only
1-week validation trial; used 7 days smartphone
app; conducted twice a 24-HDR for reference;
Carter [63], 2013, UK
Smartphone-based dietary
assessment applying FR
6-month RCT; Smartphone group; Web-based
group; Paper-based group
128 healthy overweight (BMI
>27 kg/m2 ) adults (aged 18–65)
Thomas [49], 2013, USA
Smartphone-based
self-monitoring applying FR
keeping and feedback
Pilot study, 12–24 weeks; measures: weight,
adherence, physical activity, and satisfaction;
compared to results from other primary literature.
20 overweight/obese
(25–50 kg/m2 ) adults (aged
18–70)
50 healthy adults; mean age
35; mean BMI 24
Combined approach (PDA + feedback) achieved >5%
weight loss as compared to paper based records
(P = 0.05) or electronic approach without feedback
(P = 0.09); A greater proportion of PDA groups,
compared to paper diary group, was adherent
>60% of time (P = 0.03)
Intervention group lost 3.3 ± 4.0 kg, comparison
group gained 0.9 ± 3.4 kg (P < 0.0001).
High correlation of recorded energy intake
between both approaches: day 1: r 0.77 (95% CI
0.62, 0.86), day 2: r 0.85 (95% CI 0.74, 0.91)
Adherence was significantly higher in the
smartphone group compared with the website
group and the diary group (P < 0.001); Smartphone
group showed the highest decrease in weight, BMI,
and body fat compared to the two other
approaches.
Weight-loss monitored was substantially larger
than the loss of 3–5% of initial body weight
obtained with text message only-based
interventions. Adherence to the self-monitoring
protocol was 91% (SE 3.3%) and 85% (SE 4.0%) at 12
and 24 weeks, respectively. This was substantially
higher than rates seen in other trials of behavioral
weight-loss treatment using paper diaries (e.g.
55%)
Abbreviations: PDA, personal digital assistant; RCT, randomized controlled trial; FR, food record; 24-HDR 24-hour dietary recall; FFQ, Food frequency questionnaire; BMI, body mass index
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assessment and management—Current research status and available applications, Int. J. Med. Inform. (2015),
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Table 3
Studies demonstrating the successful use of computer-supported diet management with respect to weight management.
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“Bon’App” even enable voice input. More elaborated food tracker
apps (e.g., “MyFitnessPal”, “Lose It!”, “Calorie Count”, “Fooducate”,
“FoodScanner”) provide barcode scanning possibilities using the
smartphone camera, which is supposed to facilitate the identification of branded food items.
However, the usability of these food entry options depends on
the completeness of the underlying food databases, i.e., data about
food products can only be retrieved given their listing in the food
database used by the app. Thus, the quality of a food and calorietracking app highly depends on the quality and quantity of data
available in its food database. This is why some food tracking apps,
including “Foodzy”, “FoodScanner” and “FatSecret”, provide the
opportunity for users to extend their food databases with custom
products, which are in some cases automatically included into the
underlying food database and thus made available for other users as
well. Nevertheless, even the largest current food databases are still
far from being complete and often contain only country-specific
products. To evade this problem of lacking food-related data, some
apps that implement photo-based food diaries have emerged. For
instance, with “PhotoCalorie” or “MealSnap”, users only need to
take a photo of their meal and provide a brief corresponding textual
description in order to create a food log entry, upon which corresponding nutritional values are automatically estimated. However,
user reports show that these values are often inaccurate.
Some food-related health apps aim at personalized meal planning, taking into account the user’s health and weight-loss goals
as well as previously defined food preferences. “Pocket Dietitian”
offers automated meal planning with regard to individual daily
nutrient levels. The “intelli-Diet” app creates a personal wellbalanced diet based on a list of favorite foods specified by the user
and an eating plan for each day of the week, and it even automatically generates a corresponding shopping list.
In 2009, Breton et al. [64] reviewed 204 apps available in the
iTunes app store for compliance with evidence-informed practices
in weight-loss. Of the reviewed apps, 43% provided tools for keeping
a food diary but less than 10% offered advice on meal planning.
Food nutritional databases were applied in one third of the apps
(n = 67), and only 15% of the apps (n = 30) were designed to be used
in conjunction with a website. A small fraction of 3% (n = 7) had
some type of social network integrated. Based on this study, it can
be concluded that only a small portion of commercially available
apps allowed individual meal planning based on food databases.
In a recent 2013 pilot study with 20 overweight participants
over 12–24 weeks [49], Thomas and Wing showed that a smartphone application offering self-monitoring functionalities achieved
much better effects with respect to weight-loss than only sending supportive text messages (9% weight-loss on average instead
of only 3–5%). Furthermore, they found higher adherence rates
for an app-based self-monitoring protocol (91%) as compared to
a paper-based diary (55%). For their study, the authors used the
commercially available “DailyBurn” app for tracking food intake,
weight, and physical activity combined with the self-developed
“Health-E-Call” app for texting, providing supportive videos and
other material, as well as for setting behavioral goals. This study
also highlights the need for integrated solutions, in which a mere
tracking of food intake is enhanced by additional support to further
increase the users’ motivation to adhere to the intervention. This
additional support appears to play a crucial role, similar to the support in conventional weight-loss programs accompanied by regular
meetings with physicians and nurses. Many food and diet trackers offer forum groups and enable their users to share their food
diaries with friends in order to receive support and encouragement
(e.g., through Twitter or Facebook). For example, the “SparkPeople”
app applies a game-like approach using leaderboards, prizes and
awards to encourage a “friendly competition” concerning fitness
and weight-loss. In some other cases, apps provide the opportunity
7
to connect to a health care professional. With “My Dietitian”, users
can receive customized daily feedback on their food journal from
a personal registered dietitian. Other apps, e.g. “Pocket Dietitian”,
allow users to export and email dietary reports to their physicians.
In a web-based computer-tailored intervention named FATaintPHAT to promote energy balance among 883 overweight
adolescents [47], Ezendam et al. did not find the expected reduction of BMI and waist circumference. The two-group RCT showed
only minor positive dietary behavioral effects in the short term
(4-month follow-up) but not in the long term (2-year follow-up).
The intervention concept was a non-commercial web-based educational platform that used FFQ and 24-HDR for capturing food intake
and provided goal setting, action planning and behavioral feedback,
but this approach may have lacked sufficient support and motivation tools as integrated by some of the other programs mentioned
above. Similarly, Bauer et al. [44] could not demonstrate a statistically significant reduction in BMI Standard Deviation Scores for an
SMS-based maintenance treatment with weekly self-monitoring of
data on eating, exercise and emotions in a 12-week study comprising 40 overweight subjects.
In contrast, in 2012 Anton and his colleagues were able to show
in a study with 811 participants that overweight subjects with a
high usage of a web-based tracking system for dietary assessment
and feedback lost significantly greater amounts of weight than participants with low usage (8.7% versus 5.5% of initial body weight)
[22]. The authors attributed the system’s success to its immediate feedback on reported behaviors and dietary intake, including
assessment of key behavioral indicators of adherence that may not
be available in many other current applications.
In a 2012 validation study [37], Carter et al. compared 24-HDR
conducted via phone interviews to smartphone-based food and
drink records employing a database comprising 40,000 commercial food items including generic and branded items [65] in an
obese population of 50 subjects. On an individual level, large disagreement between both approaches could be monitored, but on a
group level, taking FR on mobile phones appeared to bear potential as a diary assessment method, yielding results comparable to
the dietary recall approach. In a later pilot study in 2013 [63] with
128 overweight subjects, the authors reported significantly higher
adherence to their smartphone-based approach (92 days) as compared to web-based (35 days) and paper-based (29 days) methods.
In 2012, Lieffers et al. reviewed available studies on mobile
devices for food intake recording in healthy adult populations in
relation to general weight-loss approaches [25]. The authors differentiated between applications by means of record selection from
food databases (e.g. USDA National Nutrient Database for Standard
Reference) and picture taking in conjunction with reference objects
and annotation through text or voice input. The authors found good
correlations for both methods regarding energy and nutrient intake
in comparison with conventional methods (24-HDR, paper-based
FR).
3.3. Computer-supported dietary management for diabetes
3.3.1. Scientific approaches
Similar to obesity management, computer-aided diabetes management mainly consists of self-monitoring and education. The UK
National Institute for Health and Clinical Excellence (NICE) guidelines for management of type 2 diabetes [66] “encourage high-fiber,
low-glycemic-index sources of carbohydrate in the diet, such as
fruit, vegetables, whole grains and pulses; include low-fat dairy
products and oily fish; and control the intake of foods containing
saturated and trans fatty acids.”
In a pilot study by Arsand et al. [26], five important points were
highlighted for IT devices designed for diabetes type I and II: (1)
a complete food pick-list, (2) a smartphone touchscreen concept,
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(3) the possibility to download data to a PC, (4) editing possibilities of entries, (5) reinforcement cues, such as emoticons. The
mentioned design concepts were viewed as useful and potentially
powerful tools in both self-care and provider-supported care settings. The food pick-list should be constantly re-ordered based on
frequency of item selection, and it should allow for specifying portion and serving sizes. Participants found both positive and negative
reinforcement cues appealing and rewarding in respect of their
progress.
In a 2011 literature review and comparison of available mobile
type I and II diabetes application features against evidence-based
guidelines [67], Chomutare et al. reviewed features including
self-monitoring, social media integration, data export and communication as well as synchronization with PHR systems or patient
portals. Their literature research comprising 26 studies showed that
most approaches included some kind of PHR synchronization (69%),
insulin and medication recording (65%), diet recording (65%), and
data export and communication (62%). Most notably, Chomutare
et al. revealed that PHR synchronization was present in only 17%
of the applications available on the online markets (n = 101). Here,
the most prominent features were insulin and medication recording (62%), data export and communication (60%), diet recording
(47%), and weight management (43%). It turned out that capturing consumed food items was a highly manual task, as the users
either had to estimate carbohydrates or navigate through an extensive food hierarchy or through a menu. The authors concluded that
most approaches introduced in the literature comprised PHR integration, which was not true for most apps available on the market,
highlighting the gap between scientifically reasonable approaches
and practically available strategies. In a 2013 short review by Goyal
and Cafazzo [68], the authors concluded that a significant potential
lies in direct, real time communication between health professionals and individuals in order to be able to capture data electronically,
and thus to provide decision support more easily.
Menu planning and tools aiding in choosing dishes have shown
to be important features for diabetes patients. In a pilot study [28]
with 33 type II diabetes mellitus (T2DM) adults, Bader et al. were
able to show that web-based menu planning during a 24 week
period had the potential to lead to clinically important weight
reductions (above 5%) in more than 25% of the adherent participants.
3.3.2. Mobile apps
Mobile apps in the diabetes domain have become increasingly abundant. Important drivers for successful app development
are consumer expectations. In a 2011 review on diabetes-related
telemedicine approaches by Franc et al. [69], the authors summarized the patient expectations into three concepts: (1) An
easy to use mobile and pocket-sized system to improve the
compliance as compared to systems involving desktop computers; (2) Systems should respond immediately to patients’
questions and provide automatic assessment of carbohydrate contents through using a reliable food database, while in addition,
the devices should also guide food choices through an onboard
database; (3) Interactions with a known caregiver as a key
component for the success of telemedical systems for diabetes
care.
Besides the possibility to log food intake (carbohydrates),
most apps targeted at diabetes management allow the logging
of other relevant parameters, such as blood glucose, dosage
of insulin, blood pressure, pulse, weight, and sport activities.
“MyNetDiary Diabetes Tracker” provides a tool for the daily and
weekly analysis of the logged data to support users in improving their diet. Furthermore, it offers diet planning, allowing
users to define their individual macronutrient targets. With “Diabetes In Check”, users have access to diabetes-friendly recipes,
sample meal plans and customized daily menus, and they receive
tips and constructive feedback on a regular basis as a motivation to improve their medical condition. Such motivational
aspects also play an important role in the “mySugr Diabetes Companion” app. In this tool, users receive immediate feedback on
their entries through a virtual character called “diabetes monster”.
With respect to data management, which is important for sharing with other health professionals, most apps offer the possibility
to export a report as a printable PDF or Excel file, or even a direct
transmission of the report to a physician via e-mail. Reminders to
measure blood glucose, to take medication, or to track food and
exercise are also implemented in many diabetes management apps.
Social interaction with a community seems to be less important
than with weight-loss apps, perhaps as the personal motivation of
subjects with a disease is stronger, and the link to health professionals is usually given. Nevertheless, some diabetes apps integrate
social media interfaces. The “Glucose Buddy Diabetes Log” app
offers Facebook and Twitter functionalities, while “Diabetes In
Check” provides access to community message boards for posting
personal questions, sharing success stories and providing support
to others.
In a recent review by Eng and Lee [70], available iPhone apps
(n = 492) related to diabetes management were scrutinized based
on their summary descriptions. Most of the apps (33%) focused on
health tracking, such as blood sugar, insulin doses, and carbohydrates, involving manual entry, but only 8% of the analyzed apps
provided food reference databases. Only two apps allowed capturing blood sugar levels through glucometers directly attached to the
smartphone. Additional features were teaching/training (8%), social
blogs/forums (5%) and physician-directed apps (8%). The authors
highlighted that only the “WellDoc” system appeared suitable for
direct integration into health care workflows or Electronic Medical
Record systems. Further, only “WellDoc”, “Glooko”, and “IGBStar”
have received clearance from the US Food and Drug Administration
(FDA). The authors pointed out safety concerns about the majority
of the apps, which were non-FDA certified, although they should be
so according to safety regulations. In line with these observations,
El-Gayar and colleagues concluded in a 2013 review of commercial applications for diabetes self-management [31] that mobile
applications have the potential to positively impact diabetes selfmanagement, but also identified limitations, such as the lack of
personalized feedback, usability issues, such as difficult data entry,
and missing integration with PHR.
Arsand et al. concluded in a review article on mobile health
applications assisting patients with diabetes [27] that using mobile
phone picture diaries is useful for the identification of treatment
obstacles for type 1 diabetes mellitus (T1DM) patients. It was further suggested that the food information on phones for T2DM
should not be too fine-grained, as too much detailed information
may result in user discouragement and little user friendliness. Lee
et al. concluded in their review on mobile terminal-based tools for
diabetes diet management [33] that in order to make such mobile
tools feasible for diet management, these should enable the recording of food intake in an easy but accurate manner, and suggested
that photographs could be a meaningful strategy.
4. Integrative summary and discussion
This review aimed at providing a descriptive overview of the
current status of computer-supported diet management, integrating scientific evidence as well as highlighting important aspects of
commercially available applications and developments in this field.
Please cite this article in press as: A.G. Arens-Volland, et al., Promising approaches of computer-supported dietary
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9
Table 4
Summary of tools in computerized diet management and associated advantages and disadvantages and scientific evidence across application areas.
Type of dietary
assessment &
self-management
Dietary
management
techniques applied
Associated
advantages
Associated
drawbacks
Scientific evidence
References
Web-based
24-HDR;
self-monitoring;
FR; goal setting;
feedback;
Inexpensive;
widely accessible;
Tedious or
cumbersome FR
input due to user
interface
limitations;
[22,26,28,35,36,39,40,43,
46–48,52,53,58,62,63,72]
Mobile
FR; 24-HDR;
self-monitoring;
feedback; food
picture diary
High validity
compared to
traditional
methods; high user
acceptance and
user adherence;
effective for
weight-loss and
self-management
Expensive; only
preliminary
evidence of
effectiveness of
food picture diary.
RCT (n = 3), pilot (n = 3), usability (n = 3) trials
and one retrospective analysis in areas of
overweight/obesity, weight-loss, and diabetes.
Additionally, four validation studies have been
performed.
Study measures included dietary intake,
physical activity (minutes/week), energy
expenditure, body weight and height.
Sample sizes ranged from 9 (pilot validation
study) to 3621 subjects (retrospective
analysis), age range 18–70.
RCT (n = 4), pilot (n = 5) or usability studies
(n = 2) available in areas of overweight/obesity,
weight-loss. Only one RCT for diabetes and one
validation study. Study measures included
dietary intake, calculated calorie intake, body
weight change, adherence and satisfaction.
Sample sizes ranged from 27 to 365 subjects,
including children, adolescents, adults and
elderly.
[11,18,19,21,23,24,37,38,
41,42,44,45,49–51]
Abbreviations: 24-HDR: 24-hour dietary recall; FR: food record; RCT: randomized controlled trial.
4.1. Principal results
From a scientific viewpoint, it can be assumed that web-based
only solutions for tackling obesity in young adults are not effective
[47]. Mobile solutions are at least equally effective as traditional
paper-based methods [50], and they appear superior to traditional
approaches when allowing for personalized feedback [45]. Furthermore, Burke et al. demonstrated that with suitable functionality
(access to food database) and integration into the health care workflow (giving feedback with respect to patient actions), a high level
of adherence and weight-loss can be achieved [7,45]. This is supported by the findings of Yon et al. [50], who argued that suitable
food databases are required to achieve a satisfying user experience.
Generally, in research, FFQs and 24-HDRs are the most commonly
used tools for food intake monitoring; occasionally FRs are offered,
and barcode-scanning is hardly ever used for food record input. This
is clearly related to the fact that the commonly used food data in the
reviewed settings were most often derived from food composition
databases and only rarely from food product databases that contain information on branded food products. The end-users’ demand
for suitable food databases and patient-caregiver interaction has
also been identified by Franc et al. [69]. Existing scientific evidence
for web-based and mobile dietary management is summarized in
Table 4, also highlighting associated advantages and drawbacks.
In this respect, it is noteworthy to emphasize the general limitations of employing food composition databases for determining
nutritional and caloric composition of the finally consumed product, which may differ from the values captured in the database due
to “factory to fork” losses, i.e., following storage and kitchen procedures applied, such as freezing/thawing, mixing, chopping, heating
etc. [71]. However, this applies to all underlying databases and is
not limited to computer aided dietary assessment. Additional limitations are the natural variations of the listed food items as well as
the difficulty of the consumer to judge serving sizes.
With respect to the used app features, commercially available
apps show increased innovative functionalities, such as keeping
FR via barcode scanning and diet documentation through photographs. Promising approaches for picture-based identification of
food as well as for calorie and nutrient estimation exist in research
[18,38,41,42], but apps that implement these features are often
inaccurate. This is due to the discrepancy between the limited laboratory settings in research as compared to the varying real-life
environments. The opportunity to share information and experiences and to receive feedback and support from a community of
like-minded people seems to be an important aspect concerning
apps targeting weight-loss.
It was noticed that an integration with a PHR or in the health care
workflow is present in the literature [30,70,73], while in contrast,
applications accessible in the app stores generally do not provide
such features. At most, the apps allow for data export (e.g., via
email) to health care professionals, however, they fail to actively
engage them. The “WellDoc” app [70] is a notable exception in this
context.
With the aim to promote health-related apps of high quality,
the British National Health Service (NHS) offers an online Health
Apps Library [74]. It contains health apps from different domains
(e.g. diabetes, nutrition, heart, cancer) that have been reviewed
and approved by the NHS. Doctors are encouraged to prescribe
such apps to their patients in order to facilitate and improve their
treatment.
4.2. Limitations
As this review has integrated data from scientific publications,
the described functionalities and findings were taken only from the
published articles and were not further tested or verified. Due to the
huge number of applications already available and the extremely
rapid development of the market, combined with time constraints,
we were unable to take into account all available applications in the
present review, and we were far from being able to test all these
applications. We thus had to rely on the descriptions made in the
catalogues of the suppliers and in the corresponding test reports.
A lot of scientific and practical work with respect to computer
diet management has been carried out in the field of obesity and
diabetes. Some of the publications reviewed in this article are high
level clinical studies, but others report the results of observational
or usability studies. Scientific studies concerning diet management
in the areas of other medical conditions, such as food hypersensitivities, cancer or CVD, are rather sparse, and they are thus not
considered in this review.
5. Conclusions and perspectives
The need for well-established, reliable and affordable techniques for monitoring food intake (FFQ, 24-HDR, FR) is evident,
Please cite this article in press as: A.G. Arens-Volland, et al., Promising approaches of computer-supported dietary
assessment and management—Current research status and available applications, Int. J. Med. Inform. (2015),
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Conflicts of interest
Summary points
What was already known on the topic
• Computer-supported dietary assessment and management
technologies support the user and health care professional
in food intake monitoring.
• Various techniques are employed in different application
areas, such as weight-loss, diabetes etc., as well as in scientific and commercial settings.
None declared.
Funding
This work was supported by the ERDF (European Research and
Development Fund).
What this study added to our knowledge
Authors’ contributions
• Many computer-based approaches implement wellestablished nutritional concepts for dietary assessment.
• Both food records and barcode scanning are less prominent in research but are frequently offered within commercial
applications.
• Integration with a personal health record (PHR) or a health
care workflow is suggested in the literature but is rarely
found in commercial applications.
• Major challenges in the context of computer-supported diet
management:
• Simple, intuitive and robust user interfaces for input of food
records.
• Comprehensive and reliable food databases for packed food.
Arens-Volland was responsible for the organization and creation
of the manuscript. He performed literature search and evaluation of
articles. Spassova reviewed available mobile apps and web-based
solutions and contributed to the development and content of the
manuscript. Bohn reviewed analyses and scientific findings of scientific articles and aided in overall manuscript structuring. All
authors reviewed and contributed to the preparation of the final
manuscript.
both for collecting large data with a scientific purpose, but also for
individuals controlling their own dietary behavior. Innovative input
methods (barcode scanning, FR) that are largely available in commercial apps have so far been used only rarely in science. However,
several studies have indicated that computer-supported diet management has many advantages, including efficacy and efficiency as
well as the possibility to collect detailed nutrition-related data and
to offer in-time communication and feedback.
In our opinion, there are two major factors that will drive the
usability and acceptance of computer-supported FR: (1) the development of simple, intuitive and robust user interfaces for the input
of food records, especially for mobile diet-related apps; and (2) the
availability of comprehensive food databases for packed food that
provide reliable data. The first challenge might be tackled through
further developments in the area of picture-based food recognition,
leading to a more accurate identification of food type and serving sizes, or through the incorporation of novel approaches, such
as spectrometer-based nutrient recognition (e.g., TellSpec [75]) or
unobtrusive sensors, such as the ear-worn device “BitBite”, which
uses a microphone to recognize chewing patterns and to allow voice
input of food records [76].
Concerning the second challenge, we believe that a combination of established food composition databases and food product
databases might be a favorable solution for achieving a comprehensive food database suitable for use in computer-supported diet
management applications. On the other hand, the lacking integration and financing of available mobile apps in the health care sector
with a clear legal status is a major disadvantage that should be overcome in order to facilitate access of a broader population to such
health-supporting tools.
In summary, electronic means for dietary management have
shown to offer some advantages over traditional ones, i.e. paperbased approaches. However, it should be kept in mind that
computer-supported dietary assessment merely presents one
strategy of dietary support, and it should be ideally combined with
other means, including e.g. psychological and social support, to successfully motivate healthy behavioral changes toward e.g. weight
loss. In this context, further validation studies evaluating the effectiveness of computer-supported dietary management are required.
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